Polarimetric SAR speckle filtering and its impact on classification

Lee, J.S.; Grunes, M.R.; Grandi, G. De

doi:10.1109/igarss.1997.615335

Cited by 42 publications

(38 citation statements)

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“…In this case, speckle reduction (i.e., signal estimation) is obtained at the expense of spatial resolution. The statistics of the covariance matrix have been found to be the Wishart pdf [15], [16], [18] tr (7) where is (8) tr is the matrix trace, and represents the number of channels. In the case of a monostatic PolSAR system configuration, equals 3.…”

Section: Polarimetric Sar Data Descriptionmentioning

confidence: 99%

“…An extension of the single-channel model is not possible, as the SAR images present generally a correlation degree between them. Hence, the speckle noise nature for PolSAR data is still not known [7], [8]. SAR data modeling and SAR data filtering (i.e., information estimation) are two different aspects of the same problem, as the availability of a polarimetric noise model would make a correct signal estimation possible.…”

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confidence: 99%

“…These approaches are characterized for keeping the spatial resolution but loosing all the polarimetric information. The second group contains all those techniques based on obtaining the speckle-free input images, therefore maintaining the correlation information between them [8], [12]. This second group of techniques is basically based on an spatial processing of the SAR images, therefore, affecting the spatial resolution.…”

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confidence: 99%

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Polarimetric sar speckle noise model

López-Martínez

Fábregas

2003

IEEE Trans. Geosci. Remote Sensing

155

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Abstract-Synthetic aperture radar (SAR) data are affected by speckle noise, originated by the SAR system's coherent nature. The problem of speckle noise in one-dimensional (1-D) data is already solved, as speckle has a multiplicative characteristic. SAR polarimetry represents an extension to multidimensional data by the use of polarization wave diversity. As a consequence of the existence of a correlation degree between the SAR images, the 1-D speckle noise model cannot be extended to multidimensional SAR data. This paper is devoted to present a completely new speckle noise model for the complex covariance matrix describing polarimetric SAR data in the distributed scatterers case. As will be shown, this new model is able to identify which are the noise mechanisms in all the covariance matrix elements. The speckle noise model is validated by using real L-band polarimetric data acquired with the German E-SAR sensor.Index Terms-Covariance matrix, noise modeling, speckle noise, synthetic aperture radar (SAR) polarimetry.

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Section: Polarimetric Sar Data Descriptionmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Polarimetric sar speckle noise model

López-Martínez

Fábregas

2003

IEEE Trans. Geosci. Remote Sensing

155

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“…The aerial photographs for this area which can be used as ground-truth are provided by the TerraServer Web site [20]. In this study, the speckle filter suggested by Lee et al [21] is employed with 5 × 5 window to preserve the texture information as recommended.…”

Section: Resultsmentioning

confidence: 99%

Polarimetric SAR Image Classification Using Radial Basis Function Neural Network

İnce¹

2010

PIERS Online

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Abstract-This paper presents a robust radial basis function (RBF) network based classifier for polarimetric synthetic aperture radar (SAR) images. The proposed feature extraction process utilizes the covariance matrix, the gray level co-occurrence matrix (GLCM) based texture features, and the backscattering power (Span) combined with the H/α/A decomposition, which are projected onto a lower dimensional feature space using principal component analysis. For the classifier training two popular techniques are explored: conventional backpropagation (BP) and particle swarm optimization (PSO). By using both polarimetric covariance matrix and decomposition based pixel values and textural information (contrast, correlation, energy, and homogeneity) in the feature set, classification accuracy is improved. An experimental study is performed using the fully polarimetric San Francisco Bay and Flevoland data sets acquired by the NASA/Jet Propulsion Laboratory Airborne SAR (AIRSAR) at L-band to evaluate the performance of the proposed classifier. Classification results (in terms of confusion matrix, overall accuracy and classification map) compared with competing state of the art algorithms demonstrate the effectiveness of the proposed RBF network classifier.

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“…In addition, the coherence or correlation between the two phase components declines if changes in the forest have occurred. Lastly, the RADAR signal is also subject to constructive and destructive interference due to multiple scattering elements within a resolution cell, producing an essentially random cell to cell backscatter variation (speckle) [162] that can be reduced using multiple looks or window-based filtering [163,164].…”

Section: Radarmentioning

confidence: 99%

Understanding Forest Health with Remote Sensing-Part II—A Review of Approaches and Data Models

et al. 2017

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Stress in forest ecosystems (FES) occurs as a result of land-use intensification, disturbances, resource limitations or unsustainable management, causing changes in forest health (FH) at various scales from the local to the global scale. Reactions to such stress depend on the phylogeny of forest species or communities and the characteristics of their impacting drivers and processes. There are many approaches to monitor indicators of FH using in-situ forest inventory and experimental studies, but they are generally limited to sample points or small areas, as well as being time-and labour-intensive. Long-term monitoring based on forest inventories provides valuable information about changes and trends of FH. However, abrupt short-term changes cannot sufficiently be assessed through in-situ forest inventories as they usually have repetition periods of multiple years. Furthermore, numerous FH indicators monitored in in-situ surveys are based on expert judgement. Remote sensing (RS) technologies offer means to monitor FH indicators in an effective, repetitive and comparative way. This paper reviews techniques that are currently used for monitoring, including close-range RS, airborne and satellite approaches. The implementation of optical, RADAR and LiDAR RS-techniques to assess spectral traits/spectral trait variations (ST/STV) is described in detail. We found that ST/STV can be used to record indicators of FH based on RS. Therefore, the ST/STV approach provides a framework to develop a standardized monitoring concept for FH indicators using RS techniques that is applicable to future monitoring programs. It is only through linking in-situ and RS approaches that we will be able to improve our understanding of the relationship between stressors, and the associated spectral responses in order to develop robust FH indicators.

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Polarimetric SAR speckle filtering and its impact on classification

Cited by 42 publications

References 10 publications

Polarimetric sar speckle noise model

Polarimetric sar speckle noise model

Polarimetric SAR Image Classification Using Radial Basis Function Neural Network

Understanding Forest Health with Remote Sensing-Part II—A Review of Approaches and Data Models

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